Triple

T8145727
Position Surface form Disambiguated ID Type / Status
Subject University of Wisconsin–Stevens Point E190205 entity
Predicate abbreviation P43 FINISHED
Object UWSP
UWSP is a public university in Stevens Point, Wisconsin, known for its strong programs in natural resources, education, and the arts within the University of Wisconsin System.
 E713138 NE FINISHED

How this triple was built (4 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: UWSP | Statement: [University of Wisconsin–Stevens Point, abbreviation, UWSP]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: UWSP
Context triple: [University of Wisconsin–Stevens Point, abbreviation, UWSP]
  • A. UW
    UW is a leading Canadian public research university known for its strong engineering, computer science, and co-operative education programs.
  • B. UW
    UW is the commonly used abbreviation for the University of Warsaw, a major public research university in Poland’s capital city.
  • C. UW
    UW is a common abbreviation for the University of Wisconsin, whose athletic teams are known as the Wisconsin Badgers.
  • D. UW
    UW is the University of Wyoming, a public land-grant research university located in Laramie, Wyoming.
  • E. WSCUC
    WSCUC is a regional accrediting agency in the United States that evaluates and certifies the quality and effectiveness of senior colleges and universities.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: UWSP
Triple: [University of Wisconsin–Stevens Point, abbreviation, UWSP]
Generated description
UWSP is a public university in Stevens Point, Wisconsin, known for its strong programs in natural resources, education, and the arts within the University of Wisconsin System.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: UWSP
Target entity description: UWSP is a public university in Stevens Point, Wisconsin, known for its strong programs in natural resources, education, and the arts within the University of Wisconsin System.
  • A. UW
    UW is a leading Canadian public research university known for its strong engineering, computer science, and co-operative education programs.
  • B. UW
    UW is a common abbreviation for the University of Wisconsin, whose athletic teams are known as the Wisconsin Badgers.
  • C. UW
    UW is the University of Wyoming, a public land-grant research university located in Laramie, Wyoming.
  • D. UW
    UW is the commonly used abbreviation for the University of Warsaw, a major public research university in Poland’s capital city.
  • E. WSCUC
    WSCUC is a regional accrediting agency in the United States that evaluates and certifies the quality and effectiveness of senior colleges and universities.
  • F. None of above. chosen

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69ca82be7ba8819087de0147e9292c83 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb4447dbc48190affb0f34f6c85f5a completed March 31, 2026, 3:49 a.m.
NED1 Entity disambiguation (via context triple) batch_69cc94b0fc0481909a21f42364a92158 completed April 1, 2026, 3:44 a.m.
NEDg Description generation batch_69cc963fe2f8819098ad6a726e226189 completed April 1, 2026, 3:51 a.m.
NED2 Entity disambiguation (via description) batch_69cc977c9bf4819081c4df682e4cdf1e completed April 1, 2026, 3:56 a.m.
Created at: March 30, 2026, 5:36 p.m.